{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semantic Search with Dense Vector Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm, trange\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(\"../..\")\n",
    "import copy\n",
    "import pickle\n",
    "import numpy\n",
    "import pandas\n",
    "from aips import *\n",
    "from aips.spark import create_view_from_collection\n",
    "from pyspark.conf import SparkConf\n",
    "from pyspark.sql import SparkSession\n",
    "from aips.data_loaders.outdoors import load_dataframe\n",
    "import sentence_transformers\n",
    "import torch\n",
    "\n",
    "engine = get_engine()\n",
    "conf = SparkConf()\n",
    "conf.set(\"spark.driver.memory\", \"8g\")\n",
    "conf.set(\"spark.executor.memory\", \"8g\")\n",
    "conf.set(\"spark.dynamicAllocation.enabled\", \"true\")\n",
    "conf.set(\"spark.dynamicAllocation.executorMemoryOverhead\", \"8g\")\n",
    "spark = SparkSession.builder.appName(\"AIPS\").config(conf=conf).getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load and clean the Outdoors dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#%run 1.setting-up-the-outdoors-dataset.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc272d5ddaa949c892c36782b2714393",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b10769270242471792526f6f1e6112af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config_sentence_transformers.json:   0%|          | 0.00/122 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ff0d78aa71a49278207225cc1a51db2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/4.03k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af8d832781694880878744a85c658125",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b7a1c4065b8404091624daad0988f08",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "576a45b8e08441028a432e267a4a1002",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3a31ef21d3e4572b59fbca0dd92cc6e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "513bbd314c8842ec904b39f336b784be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4eb5c45e6710489888b25ef11e9aade4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b444390e95c84e008ece5c1bd6cce144",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6bc93d1bedb44019d37f5770a87d6ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6d2aa6a0cad4437b2f98a7125a3bc27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "349c68a8cc0e404ea667cb976d510ab2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "transformer = SentenceTransformer(\"roberta-base-nli-stsb-mean-tokens\")\n",
    "cache_name = \"outdoors_semantic_search_embeddings\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_embeddings(texts, cache_name, ignore_cache=False):\n",
    "    cache_file_name = f\"data/embeddings/{cache_name}.pickle\"\n",
    "    if ignore_cache or not os.path.isfile(cache_file_name):\n",
    "        embeddings = transformer.encode(texts)\n",
    "        os.makedirs(os.path.dirname(cache_file_name), exist_ok=True)\n",
    "        with open(cache_file_name, \"wb\") as fd:\n",
    "            pickle.dump(embeddings, fd)\n",
    "    else:\n",
    "        with open(cache_file_name, \"rb\") as fd:\n",
    "            embeddings = pickle.load(fd)\n",
    "    return embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rank_similarities(phrases, similarities, name=None):\n",
    "    a_phrases = []\n",
    "    b_phrases = []\n",
    "    scores = []\n",
    "    for a in range(len(similarities) - 1):\n",
    "        for b in range(a + 1, len(similarities)):\n",
    "            a_phrases.append(phrases[a])\n",
    "            b_phrases.append(phrases[b])\n",
    "            scores.append(float(similarities[a][b]))\n",
    "    dataframe = pandas.DataFrame({\"score\": scores,\n",
    "                                  \"phrase a\": a_phrases, \"phrase b\": b_phrases})\n",
    "    dataframe = dataframe.sort_values(by=[\"score\"], ascending=False,\n",
    "                                    ignore_index=True)\n",
    "    dataframe[\"idx\"] = dataframe.index\n",
    "    return dataframe.reindex(columns=[\"idx\", \"score\", \"phrase a\", \"phrase b\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listing 13.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of embeddings: 5331\n",
      "Dimensions per embedding: 768\n"
     ]
    }
   ],
   "source": [
    "outdoors_dataframe = load_dataframe(\"data/outdoors/posts.csv\")\n",
    "titles = outdoors_dataframe.rdd.map(lambda x: x.title).collect()\n",
    "titles = list(filter(None, titles))\n",
    "embeddings = get_embeddings(titles, cache_name)\n",
    "\n",
    "print(f\"Number of embeddings: {len(embeddings)}\")\n",
    "print(f\"Dimensions per embedding: {len(embeddings[0])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the top similarities for the titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_embedding(embedding):\n",
    "    normalized = numpy.divide(embedding, numpy.linalg.norm(embedding))\n",
    "    return list(map(float, normalized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>idx</th>\n",
       "      <th>score</th>\n",
       "      <th>phrase a</th>\n",
       "      <th>phrase b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.846395</td>\n",
       "      <td>How do I recognize if someone is suffering from hypothermia?</td>\n",
       "      <td>How should I treat hypothermia?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.811995</td>\n",
       "      <td>How should I treat poison ivy?</td>\n",
       "      <td>What can I do to prevent getting poison ivy?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.800817</td>\n",
       "      <td>What is the difference between the different types of snowboards? (all-mountain/freestyle/freeride/etc)</td>\n",
       "      <td>What is the difference between camber and rocker shaped snowboards?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.794242</td>\n",
       "      <td>How do I tie a sleeping bag to my backpack?</td>\n",
       "      <td>What is the best way to store my sleeping bag for long periods of time?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.790016</td>\n",
       "      <td>What should I look for if I want to buy a winter-proofed tent?</td>\n",
       "      <td>What is the best way to store my tent?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.753913</td>\n",
       "      <td>How do I set a top rope anchor?</td>\n",
       "      <td>How do I inspect a climbing rope?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.745218</td>\n",
       "      <td>What is the safest way to purify water?</td>\n",
       "      <td>What are the different methods to purify water?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.710362</td>\n",
       "      <td>What do I need to look for in good, quality hiking boots?</td>\n",
       "      <td>What is the difference between men's and women's hiking boots?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.704151</td>\n",
       "      <td>What to look for in a durable, 3-season sleeping bag?</td>\n",
       "      <td>What is the best way to store my sleeping bag for long periods of time?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.698881</td>\n",
       "      <td>How should I check that the anchor is secure when I anchor a small yacht off unfamiliar land?</td>\n",
       "      <td>How do I set a top rope anchor?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find the pairs with the highest dot product scores\n",
    "normalized_embeddings = list(map(normalize_embedding, embeddings))\n",
    "similarities = sentence_transformers.util.dot_score(normalized_embeddings[0:100], normalized_embeddings[0:100])\n",
    "\n",
    "comparisons = rank_similarities(titles, similarities)\n",
    "display(HTML(comparisons[:10].to_html(index=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/IPython/lib/pretty.py:778: FutureWarning: Using repr(plot) to draw and show the plot figure is deprecated and will be removed in a future version. Use plot.show().\n"
     ]
    },
    {
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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{<Figure Size: (640 x 480)>}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fix rendering of this image\n",
    "from plotnine import *\n",
    "{\n",
    "    ggplot(comparisons, aes(\"idx\", \"score\")) +\n",
    "    geom_point(alpha=.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/IPython/lib/pretty.py:778: FutureWarning: Using repr(plot) to draw and show the plot figure is deprecated and will be removed in a future version. Use plot.show().\n"
     ]
    },
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{<Figure Size: (640 x 480)>}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotnine import *\n",
    "{\n",
    "    ggplot(comparisons, aes(\"idx\", \"score\")) +\n",
    "    geom_violin(color=\"blue\") + \n",
    "    scale_y_continuous(limits=[-0.4, 1.0], breaks=[-0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listing 13.17\n",
    "\n",
    "\n",
    "### Quickly matching vectors at query time\n",
    "\n",
    "Now that we can get and compare concept embeddings, we need to be able to search these embeddings efficiently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nmslib\n",
    "# initialize a new index, using a HNSW index on Dot Product\n",
    "titles_index = nmslib.init(method=\"hnsw\", space=\"negdotprod\")\n",
    "normalized_embeddings = list(map(normalize_embedding, embeddings))\n",
    "titles_index.addDataPointBatch(normalized_embeddings)\n",
    "titles_index.createIndex(print_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listing 13.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dedup these two functions from 13.3\n",
    "def print_labels(prefix, matches):\n",
    "    display(HTML(f\"<h4>Results for: <em>{prefix}</em></h4>\"))\n",
    "    for l, d in matches:\n",
    "        print(str(int(d * 1000) / 1000), \"|\", l)\n",
    "        \n",
    "def embedding_search(index, query, phrases, k=20, min_similarity=0.75):\n",
    "    matches = []\n",
    "    query_embedding = transformer.encode(query, convert_to_tensor=True)\n",
    "    query_embedding = normalize_embedding(query_embedding)\n",
    "    ids, distances = index.knnQuery(query_embedding, k=k)\n",
    "    for i in range(len(ids)):\n",
    "        distance = distances[i] * -1\n",
    "        if distance > min_similarity:\n",
    "            matches.append((phrases[ids[i]], distance))\n",
    "    if not len(matches):\n",
    "        matches.append((phrases[ids[1]], distances[1] * -1))\n",
    "    return matches\n",
    "\n",
    "def semantic_search(query, phrases, log=False):\n",
    "    results = embedding_search(titles_index, query, phrases,\n",
    "                               k=5, min_similarity=0.6)\n",
    "    if log:\n",
    "        print_labels(query, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Results for: <em>mountain hike</em></h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.723 | How is elevation gain and change measured for hiking trails?\n",
      "0.715 | How do I Plan a Hiking Trip to Rocky Mountain National Park, CO\n",
      "0.698 | Hints for hiking the west highland way\n",
      "0.694 | New Hampshire A.T. Section Hike in May? Logistics and Trail Conditions\n",
      "0.678 | Long distance hiking trail markings in North America or parts thereof\n"
     ]
    }
   ],
   "source": [
    "semantic_search(\"mountain hike\", titles, log=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listing 13.19\n",
    "Perform vector search utilizing our configured search engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cache_name = \"all_outdoors_title_embeddings\"\n",
    "\n",
    "def display_results(query, search_results):\n",
    "    print_labels(query, [(d[\"title\"], d[\"score\"])\n",
    "                         for d in search_results])\n",
    "\n",
    "def index_outdoor_title_embeddings():\n",
    "    create_view_from_collection(engine.get_collection(\"outdoors\"),\n",
    "                                \"outdoors\")\n",
    "    outdoors_dataframe = spark.sql(\"\"\"SELECT id, title FROM outdoors\n",
    "                                      WHERE title IS NOT NULL\"\"\")\n",
    "    print(f\"Calculating embeddings for {outdoors_dataframe.count()} docs.\")\n",
    "    ids = outdoors_dataframe.rdd.map(lambda x: x.id).collect()\n",
    "    titles = outdoors_dataframe.rdd.map(lambda x: x.title).collect()\n",
    "    embeddings = list(map(normalize_embedding,\n",
    "                          get_embeddings(titles, cache_name)))\n",
    "    embeddings_dataframe = spark.createDataFrame(zip(ids, titles, embeddings),\n",
    "                                   schema=[\"id\", \"title\", \"title_embedding\"])\n",
    "    \n",
    "    collection = engine.create_collection(\"outdoors_with_embeddings\")\n",
    "    print(f\"Writing {embeddings_dataframe.count()} docs to \\\"{collection.name}\\\" collection\")\n",
    "    collection.write(embeddings_dataframe)\n",
    "    return collection\n",
    "        \n",
    "def semantic_search_with_engine(collection, query, limit=10):\n",
    "    query_vector = transformer.encode(query)\n",
    "    query_vector = normalize_embedding(query_vector)\n",
    "    request = {\"query\": query_vector,\n",
    "               \"query_fields\": [\"title_embedding\"],\n",
    "               \"return_fields\": [\"title\", \"score\", \"title_embedding\"],\n",
    "               \"quantization_size\": \"FLOAT32\",\n",
    "               \"limit\": limit}\n",
    "    response = collection.search(**request)    \n",
    "    return response[\"docs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating embeddings for 5331 docs.\n",
      "Wiping \"outdoors_with_embeddings\" collection\n",
      "Creating \"outdoors_with_embeddings\" collection\n",
      "Status: Success\n",
      "Writing 5331 docs to \"outdoors_with_embeddings\" collection\n",
      "Successfully written 5331 documents\n"
     ]
    }
   ],
   "source": [
    "embeddings_collection = index_outdoor_title_embeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Results for: <em>mountain hike</em></h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.723 | How is elevation gain and change measured for hiking trails?\n",
      "0.715 | How do I Plan a Hiking Trip to Rocky Mountain National Park, CO\n",
      "0.698 | Hints for hiking the west highland way\n",
      "0.694 | New Hampshire A.T. Section Hike in May? Logistics and Trail Conditions\n",
      "0.678 | Long distance hiking trail markings in North America or parts thereof\n",
      "0.671 | How far is a reasonable distance for someone to hike on their first trip?\n",
      "0.67 | Vancouver Sunrise Hike Advice?\n",
      "0.668 | How to prepare on hiking routes?\n",
      "0.667 | Camping on top of a mountain\n",
      "0.662 | Advice for first Grand Canyon Hike for Eastern Hikers\n"
     ]
    }
   ],
   "source": [
    "query = \"mountain hike\"\n",
    "search_results = semantic_search_with_engine(embeddings_collection, query)\n",
    "display_results(query, search_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listing 13.28\n",
    "Rerank search results with cross-encoder\n",
    "<a id='listing-13.28'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rerank(results, scores):\n",
    "    for i, score in enumerate(scores):\n",
    "        results[i][\"score\"] = score\n",
    "    return sorted(results, key=lambda doc: doc[\"score\"], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "8751871a67024df690f2641810bfad1f",
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    {
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       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5eed98693bd4d82ad0a3f1b69b27237",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "549e42e79430477496694a06a9484c6a",
       "version_major": 2,
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      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>Results for: <em>mountain hike</em></h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.578 | What constitutes mountain exposure when hiking or scrambling?\n",
      "0.337 | What are some of the winter hiking trails rated difficult in Colorado's Rocky Mountain National Park that don't require technical climbing?\n",
      "0.317 | Where in the US can I find green mountains to hike like in Scotland, such as Dalveen Pass?\n",
      "0.213 | Appropriate terms for activities like camping, hiking, trekking, mountaineering, wilderness survival?\n",
      "0.104 | Camping on top of a mountain\n",
      "0.102 | How do I Plan a Hiking Trip to Rocky Mountain National Park, CO\n",
      "0.093 | What considerations are required for making a hiking ascent of Mount Othrys, Greece\n",
      "0.073 | Are there any easy hiking daytrips up mountains in Lofoten, Norway?\n",
      "0.053 | First time snow hiking\n",
      "0.049 | Advice for first Grand Canyon Hike for Eastern Hikers\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import CrossEncoder\n",
    "cross_encoder = CrossEncoder(\n",
    "  \"cross-encoder/ms-marco-MiniLM-L-6-v2\")\n",
    "\n",
    "query = \"mountain hike\"\n",
    "search_results = semantic_search_with_engine(\n",
    "       embeddings_collection, query, limit=50)\n",
    "pairs_to_score = [[query, doc[\"title\"]]\n",
    "                  for doc in search_results]\n",
    "cross_scores = cross_encoder.predict(pairs_to_score,\n",
    "                  activation_fct=torch.nn.Sigmoid())\n",
    "search_results = rerank(search_results, cross_scores)\n",
    "display_results(query, search_results[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up next: [The different quantization techniques](5.quantization.ipynb)"
   ]
  }
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